tests.py 文件源码

def test_dT_impact(xvals, f, nmpc, sim, start=0.1, end=0.8, step=0.02, ymax=200,
                   sample_size=100, label=None):
    """Used to generate Figure XX of the paper."""
    c = raw_input("Did you remove iter/time caps in IPOPT settings? [y/N] ")
    if c.lower() not in ['y', 'yes']:
        print "Then go ahead and do it."
        return
    stats = [Statistics() for _ in xrange(len(xvals))]
    fails = [0. for _ in xrange(len(xvals))]
    pylab.ion()
    pylab.clf()
    for (i, dT) in enumerate(xvals):
        f(dT)
        for _ in xrange(sample_size):
            nmpc.on_tick(sim)
            if 'Solve' in nmpc.nlp.return_status:
                stats[i].add(nmpc.nlp.solve_time)
            else:  # max CPU time exceeded, infeasible problem detected, ...
                fails[i] += 1.
    yvals = [1000 * ts.avg if ts.avg is not None else 0. for ts in stats]
    yerr = [1000 * ts.std if ts.std is not None else 0. for ts in stats]
    pylab.bar(
        xvals, yvals, width=step, yerr=yerr, color='y', capsize=5,
        align='center', error_kw={'capsize': 5, 'elinewidth': 5})
    pylab.xlim(start - step / 2, end + step / 2)
    pylab.ylim(0, ymax)
    pylab.grid(True)
    if label is not None:
        pylab.xlabel(label, fontsize=24)
    pylab.ylabel('Comp. time (ms)', fontsize=20)
    pylab.tick_params(labelsize=16)
    pylab.twinx()
    yfails = [100. * fails[i] / sample_size for i in xrange(len(xvals))]
    pylab.plot(xvals, yfails, 'ro', markersize=12)
    pylab.plot(xvals, yfails, 'r--', linewidth=3)
    pylab.xlim(start - step / 2, end + step / 2)
    pylab.ylabel("Failure rate [%]", fontsize=20)
    pylab.tight_layout()